Model Conditioned Data Elasticity in Path Analysis: Assessing the “Confoundability” of Model/Data Characteristics

Dustin Fife, Joseph Lee Rodgers, Jorge L. Mendoza

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, & Glaser, 2002; Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011; Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). Recent developments (e.g., Preacher, 2006; Roberts & Pashler, 2000, 2002) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the “confoundability” of data/model combinations (see MacCallum, Wegener, Uchino, & Fabrigar, 1993). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.

Original languageEnglish (US)
Pages (from-to)597-613
Number of pages17
JournalMultivariate Behavioral Research
Volume49
Issue number6
DOIs
StatePublished - Nov 2 2014

Fingerprint

Path Analysis
Elasticity
Data Model
Model
Wetlands
Structural Equation Modeling
Model Complexity
Linear Models
Hilbert
Research Personnel
Linear Model
Statistics
Research

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Cite this

@article{c47639a30b8741de9cd7648fbb7aea11,
title = "Model Conditioned Data Elasticity in Path Analysis: Assessing the “Confoundability” of Model/Data Characteristics",
abstract = "Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, & Glaser, 2002; Heene, Hilbert, Draxler, Ziegler, & B{\"u}hner, 2011; Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). Recent developments (e.g., Preacher, 2006; Roberts & Pashler, 2000, 2002) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the “confoundability” of data/model combinations (see MacCallum, Wegener, Uchino, & Fabrigar, 1993). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.",
author = "Dustin Fife and Rodgers, {Joseph Lee} and Mendoza, {Jorge L.}",
year = "2014",
month = "11",
day = "2",
doi = "10.1080/00273171.2014.948608",
language = "English (US)",
volume = "49",
pages = "597--613",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Psychology Press Ltd",
number = "6",

}

Model Conditioned Data Elasticity in Path Analysis : Assessing the “Confoundability” of Model/Data Characteristics. / Fife, Dustin; Rodgers, Joseph Lee; Mendoza, Jorge L.

In: Multivariate Behavioral Research, Vol. 49, No. 6, 02.11.2014, p. 597-613.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Model Conditioned Data Elasticity in Path Analysis

T2 - Assessing the “Confoundability” of Model/Data Characteristics

AU - Fife, Dustin

AU - Rodgers, Joseph Lee

AU - Mendoza, Jorge L.

PY - 2014/11/2

Y1 - 2014/11/2

N2 - Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, & Glaser, 2002; Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011; Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). Recent developments (e.g., Preacher, 2006; Roberts & Pashler, 2000, 2002) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the “confoundability” of data/model combinations (see MacCallum, Wegener, Uchino, & Fabrigar, 1993). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.

AB - Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, & Glaser, 2002; Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011; Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). Recent developments (e.g., Preacher, 2006; Roberts & Pashler, 2000, 2002) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the “confoundability” of data/model combinations (see MacCallum, Wegener, Uchino, & Fabrigar, 1993). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.

UR - http://www.scopus.com/inward/record.url?scp=84914115697&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84914115697&partnerID=8YFLogxK

U2 - 10.1080/00273171.2014.948608

DO - 10.1080/00273171.2014.948608

M3 - Article

AN - SCOPUS:84914115697

VL - 49

SP - 597

EP - 613

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

IS - 6

ER -